A simple but effective approach for kernel construction consists of substi- tuting the Euclidean distance in ordinary kernel functions by the problem specific.

A simple but effective approach for kernel construction consists of substituting the Euclidean distance in ordinary kernel functions by the problem specific ...

Distance Substitution Kernel: A Distance Substitution Kernel (DSK) allows to use of different distance-based measures for computing the string similarity ...

Abstract. During recent years much effort has been spent in incorporating prob- lem specific a-priori knowledge into kernel methods for machine learning.

A simple but effective approach for kernel construction consists of substituting the Euclidean distance in ordinary kernel functions by the problem specific ...

Feb 14, 2018 · We show that functions in the corresponding Reproducing Kernel Hilbert Space (RKHS) are Lipschitz-continuous w.r.t. the given distance metric.

Apr 14, 2015 · An easy way to turn a distance function d into a similarity function s is to compute: s=e−γ∗d. I believe that this is also what is done with the RBF kernel.

In this paper, we consider the problem of adapt- ing the kernel so that it becomes more similar to the so-called ideal kernel. We formulate this as a distance ...

A method is described which, like the kernel trick in support vector ma- chines (SVMs), lets us generalize distance-based algorithms to operate.

Learning with Distance Substitution Kernels. B. Haasdonk, and C. Bahlmann. DAGM-Symposium, volume 3175 of Lecture Notes in Computer Science, page 220-227 ...